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Noise pollution

About: Noise pollution is a research topic. Over the lifetime, 4455 publications have been published within this topic receiving 67192 citations.


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Journal ArticleDOI
TL;DR: The Raspberry Pi platforms are observed to be a feasible low-cost alternative to increase the spatial-temporal resolution, whereas Tmote-Invent nodes do not confirm their suitability due to their limited memory and calibration issues.
Abstract: Noise pollution caused by vehicular traffic is a common problem in urban environments that has been shown to affect people's health and children's cognition. In the last decade, several studies have been conducted to assess this noise, by measuring the equivalent noise pressure level (called L eq ) to acquire an accurate sound map using wireless networks with acoustic sensors. However, even with similar values of L eq , people can feel the noise differently according to its frequency characteristics. Thus, indexes, which can express people's feelings by subjective measures, are required. In this paper, we analyze the suitability of using the psychoacoustic metrics given by the Zwicker's model, instead of just only considering L eq . The goal is to evaluate the hardware limitations of a low-cost wireless acoustic sensor network that is used to measure the annoyance, using two types of commercial and off-the-shelf sensor nodes, Tmote-Invent nodes and Raspberry Pi platforms. Moreover, to calculate the parameters using these platforms, different simplifications to the Zwicker's model based on the specific features of road traffic noise are proposed. To validate the different alternatives, the aforementioned nodes are tested in a traffic congested area of Valencia City in a vertical and horizontal network deployment. Based on the results, it is observed that the Raspberry Pi platforms are a feasible low-cost alternative to increase the spatial-temporal resolution, whereas Tmote-Invent nodes do not confirm their suitability due to their limited memory and calibration issues.

95 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigate the intersensory perceptions of noise barrier performance in terms of the spectral characteristics of noise reduction combined with visual impressions of five different barrier types: aluminum, timber, translucent acrylic, concrete, and vegetated barriers.

94 citations

Proceedings ArticleDOI
15 Mar 1999
TL;DR: The experimental results show that the line spectral frequencies (LSFs) are robust features in distinguishing the different classes of noises.
Abstract: Background environmental noises degrade the performance of speech-processing systems (e.g. speech coding, speech recognition). By modifying the processing according to the type of background noise, the performance can be enhanced. This requires noise classification. In this paper, four pattern-recognition frameworks have been used to design noise classification algorithms. Classification is done on a frame-by-frame basis (e.g. once every 20 ms). Five commonly encountered noises in mobile telephony (i.e. car, street, babble, factory, and bus) have been considered in our study. Our experimental results show that the line spectral frequencies (LSFs) are robust features in distinguishing the different classes of noises.

94 citations

Journal ArticleDOI
TL;DR: A theoretical model which explains noise annoyance based on the psychological stress theory is empirically tested and indicates that concern about the negative health effects of noise and pollution, perceived disturbance, and perceived control and coping capacity are the most important variables that explain noise annoyance.
Abstract: Previous research has stressed the relevance of nonacoustical factors in the perception of aircraft noise. However, it is largely empirically driven and lacks a sound theoretical basis. In this paper, a theoretical model which explains noise annoyance based on the psychological stress theory is empirically tested. The model is estimated by applying structural equation modeling based on data from residents living in the vicinity of Amsterdam Airport Schiphol in The Netherlands. The model provides a good model fit and indicates that concern about the negative health effects of noise and pollution, perceived disturbance, and perceived control and coping capacity are the most important variables that explain noise annoyance. Furthermore, the model provides evidence for the existence of two reciprocal relationships between (1) perceived disturbance and noise annoyance and (2) perceived control and coping capacity and noise annoyance. Lastly, the model yielded two unexpected results. Firstly, the variables noise sensitivity and fear related to the noise source were unable to explain additional variance in the endogenous variables of the model and were therefore excluded from the model. And secondly, the size of the total effect of noise exposure on noise annoyance was relatively small. The paper concludes with some recommended directions for further research.

94 citations

Journal ArticleDOI
TL;DR: The development of a model for assessing TRAffic Noise EXposure (TRANEX) in an open-source geographic information system so that the treatment of source geometry, traffic information and receptors matched as closely as possible to that of the air pollution modelling being undertaken in the TRAFFIC project.
Abstract: This paper describes the development of a model for assessing TRAffic Noise EXposure (TRANEX) in an open-source geographic information system. Instead of using proprietary software we developed our own model for two main reasons: 1) so that the treatment of source geometry, traffic information (flows/speeds/spatially varying diurnal traffic profiles) and receptors matched as closely as possible to that of the air pollution modelling being undertaken in the TRAFFIC project, and 2) to optimize model performance for practical reasons of needing to implement a noise model with detailed source geometry, over a large geographical area, to produce noise estimates at up to several million address locations, with limited computing resources. To evaluate TRANEX, noise estimates were compared with noise measurements made in the British cities of Leicester and Norwich. High correlation was seen between modelled and measured LAeq,1hr (Norwich: r?=?0.85, p?=?.000; Leicester: r?=?0.95, p?=?.000) with average model errors of 3.1?dB. TRANEX was used to estimate noise exposures (LAeq,1hr, LAeq,16hr, Lnight) for the resident population of London (2003-2010). Results suggest that 1.03 million (12%) people are exposed to daytime road traffic noise levels???65?dB(A) and 1.63 million (19%) people are exposed to night-time road traffic noise levels???55?dB(A). Differences in noise levels between 2010 and 2003 were on average relatively small: 0.25?dB (standard deviation: 0.89) and 0.26?dB (standard deviation: 0.87) for LAeq,16hr and Lnight. Display Omitted Adaptation of the Calculation of Road Traffic Noise method for exposure assessment.Freely available open-source software (R with PostgreSQL and GRASS GIS).Model estimates compared well to noise measurements (r: ~0.85-0.95).Noise level exposures modelled for 8.61 million London residents (2003-2010).Over 1 million residents exposed to high daytime and night-time noise levels.

94 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023195
2022391
2021227
2020216
2019231
2018235